infrastruktura: Data and information visualization

Evidence: 100 • authority: 72

Data and information visualization (data viz/vis or info viz/vis) is the practice of designing and creating graphic or visual representations of quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. These visualizations are intended to help a target audience visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data. When intended for the public to convey a concise version of information in an engaging manner, it is ty…

Evidence: 40 • authority: 72

Technical geography is the branch of geography that involves using, studying, and creating tools to obtain, analyze, interpret, understand, and communicate spatial information. The spatial data types a technical geographer employs may vary widely, including human and physical geography topics, with the common thread being the techniques and philosophies employed. To accomplish this, technical geographers often create their own software or scripts, which can then be applied more broadly by others. They may also explore applying techniques developed for one application to another unrelated topic, such as applying Kriging, originally developed for mining, to disciplines as diverse as real-esta…

Evidence: 31 • authority: 80

How to understand and interpret clinical data

Evidence: 31 • authority: 80

Andy Kirk Introduces the Explore Explain Podcast and Shares Insights on Data Visualization

Evidence: 31 • authority: 80

Data Visualization: AI - Enhanced Visualization Tools to Better Interpret Complex Data Patterns

Evidence: 31 • authority: 88

Advanced volume visualization—new ways to explore, analyze, and interpret seismic data. Tracing fibrous objects such as well paths or channels through a dense 3-D volume is difficult in geologic/geophysical image analysis. Traditional methods involve detecting outlines of fibers in 2-D volume slices and reconstructing 3-D models from the 2-D information. This paper discusses using a virtual environment to solve this problem.

Evidence: 31 • authority: 92

Collaborative Gameplay for Children's Self-Efficacy in Computational Thinking: Qualitative Cross-Sectional Design Science Research Case Study.. Background Despite science, technology, engineering, and mathematics (STEM) fields driving socioeconomic development and career prospects, they continue to struggle with low enrollment and high dropout rates. An important predictor of academic performance and career persistence is self-efficacy, which is the belief in one's ability to succeed. Research indicates that collaboration improves learning outcomes and is associated with improved STEM motivation. Computational thinking (CT) is also increasingly valued in STEM education due to its reliance o…

Evidence: 31 • authority: 92

Roadmap for using large language models (LLMs) to accelerate cross-disciplinary research with an example from computational biology.. Large language models (LLMs) are powerful artificial intelligence (AI) tools transforming how research is conducted. However, their use in research has been met with skepticism, due to concerns about hallucinations, biases and potential harms to research. These emphasize the importance of clearly understanding the strengths and weaknesses of LLMs to ensure their effective and responsible use. Here, we present a roadmap for integrating LLMs into cross-disciplinary research, where effective communication, knowledge transfer and collaboration across diverse fiel…

Evidence: 31 • authority: 72

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing software. Data with many entries (rows) offers greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data sources. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data that have only volume, velocity, and variety can pose challenges in sampling. A fourth concept, veracit…

Evidence: 31 • authority: 72

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays an important role in making decisions more scientific and helping businesses operate more effectively. It is widely used in fields such as business analytics, healthcare, and artificial intelligence to extract meaningful insights from data. Data mining is a particular data analysi…

Evidence: 31 • authority: 92

Human-Centered Design of a Multistakeholder Reporting Dashboard for Disseminating HIV Implementation Research in the Ending the HIV Epidemic Initiative: Development and Formative Evaluation Study.. Background Interactive dashboards have expanded public access to HIV surveillance and programmatic data, but existing tools do not synthesize implementation research evidence into formats accessible to the diverse stakeholders who could use it. Designing such tools requires deliberate alignment with users' needs, language, and decision-making contexts. Objective We describe the human-centered design (HCD) process used to develop and conduct a formative evaluation of the Ending the HIV Epidemic (E…

Evidence: — • authority: 72

Biological data visualization is a branch of bioinformatics concerned with the application of computer graphics, scientific visualization, and information visualization to different areas of the life sciences. This includes visualization of sequences, genomes, alignments, phylogenies, macromolecular structures, systems biology, microscopy, and magnetic resonance imaging data. Software tools used for visualizing biological data range from simple, standalone programs to complex, integrated systems. An emerging trend is the blurring of boundaries between the visualization of 3D structures at atomic resolution, the visualization of larger complexes by cryo-electron microscopy, and the visualiza…